CFD with population balance model to predict droplet size distribution in submerged turbulent multiphase jets
Bibliographic record
Abstract
During deepwater oil spill events, oil is released into a relatively stagnant environment (ocean water) in an uncontrolled manner. The oil phase initially emerges as a jet and the gushing oil loses its momentum energy and results in entrainment of surrounding water. The shear interaction between the oil mass and the ambient fluid results in generation of droplets with wide size distribution. In this study, we present a numerical model for predicting the droplet size distribution resulting from the interaction of turbulent oil jets with the surrounding quiescent environment. We achieve this objective by integrating traditional multiphase CFD models with a population balance approach. The developed model has been validated against the experimental observations reported in Johansen et al.[12] The ‘Mixture model’ has been employed for evaluating flow fields in the system. We restrict our study to the atomization regime, where the droplet disintegration process has a greater significance over the competing coalescence mechanism. The population balance equation has been solved using the ‘Class method’ and the disintegration of droplets has been modelled by including the breakage kernel suggested by Lehr.27 The developed model has been used to analyze the effect of dispersed (oil) phase flow rates, the presence of dispersants, and the presence of air in the jet phase on the overall size distribution of oil droplets. We also present a case which compares the droplet size distributions obtained by using the flow field evaluated by a more rigorous Eulerian Two‐Fluid model over Mixture model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".